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A differential privacy protecting K-means clustering algorithm based on contour coefficients

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  • Yaling Zhang
  • Na Liu
  • Shangping Wang

Abstract

This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved differential privacy protecting K-means clustering algorithm was raised in this paper. The improved algorithm uses the contour coefficients to quantitatively evaluate the clustering effect of each iteration and add different noise to different clusters. In order to be adapted to the huge number of data, this paper provides an algorithm design in MapReduce Framework. Experimental finding shows that the new algorithm improves the availability of the algorithm clustering results under the condition of ensuring individual privacy without significantly increasing its operating time.

Suggested Citation

  • Yaling Zhang & Na Liu & Shangping Wang, 2018. "A differential privacy protecting K-means clustering algorithm based on contour coefficients," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    • Handle: RePEc:plo:pone00:0206832
    DOI: 10.1371/journal.pone.0206832
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    Cited by:

    1. Yaling Zhang & Jin Han, 2021. "Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.
    2. Sadia Basar & Mushtaq Ali & Gilberto Ochoa-Ruiz & Mahdi Zareei & Abdul Waheed & Awais Adnan, 2020. "Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-21, October.
    3. Hossam M J Mustafa & Masri Ayob & Mohd Zakree Ahmad Nazri & Graham Kendall, 2019. "An improved adaptive memetic differential evolution optimization algorithms for data clustering problems," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-28, May.
    4. Zhao, Laijun & Li, Deqiang & Guo, Xiaopeng & Xue, Jian & Wang, Chenchen & Sun, Wenjun, 2021. "Cooperation risk of oil and gas resources between China and the countries along the Belt and Road," Energy, Elsevier, vol. 227(C).

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